Seminar: Federated Learning in Healthcare (WiSe2020)

Master Seminar (IN2107, IN4410) (2 SWS, 5 ECTS) offered for BioMedical Computing (BMC) program at the Chair for Computer Aided Medical Procedures and Augmented Reality, TU Munich

Organizers: Dr. Shadi Albarqouni, Helmholtz AI and TU Munich, and Prof. Nassir Navab, TU Munich.


- 17-12-2020: The deadline to submit the blog post is moved to 1st. Feb. 2021.
- 05-11-2020: Our seminar will be held online via Zoom. Password is communicated to the participants through TUMonline. - 30-09-2020: Your name has been assigned to one of our sessions. Please pin the date on your calendar.
- 30-09-2020: Two invited talks were just arranged. Thanks to the invited speakers!
- 21-09-2020: We are still working on the schedule.
- 21-09-2020: We extended the time slots of the presentations from 20 to 30 mins (See the requirements)
- 21-09-2020: Papers are assigned to the students according to their preferences (See the list of Topics and Material Table).
- 22-07-2020: Registration is done via the matching system. You need to send a motivation letter with the subject "FLH_Motivatoon" to Dr. Shadi Albarqouni in order to get a higher ranking in the matching system from our side
- 14-07-2020: Register in advance for this meeting here. After registering, you will receive a confirmation email containing information about joining the meeting.
- 08-07-2020: Preliminary meeting: Thursday, 16.07.2020 (10:00-11:00) Friday, 17.07.2020 (11:00-12:00) in virtual meeting room (zoom).
- 08-07-2020: Contact information-If you have any question about this seminar, please feel free to contact Dr. Shadi Albarqouni
- 08-07-2020: Website is up!


Following the great success of our on-going seminar on Deep Learning for Medical Applications, we would like to discuss advanced topics that are quite relevant to Federated Learning which becomes an interesting and hot research direction in the community. In simple words, Federated Learning enables training models at the client-side while preserving their privacy, and aggregates the knowledge from the nodes to learn a global model. The interesting part here that the data are kept private and not transmitted to any other nodes. Instead, the characteristics (e.g. parameters) of the global model are shared with the clients, and once the training is done locally, the characteristics are sent back to the global one for aggregation. This learning paradigm has been received quite nicely in the community, in particular, for sensitive domains, e.g. Healthcare. To push this momentum, we proposed, together with our academia and industry partners, a workshop on Federated, Collaborative, and Distributed Learning in the International Conference on Medical Image Computing and Computer-Aided Intervention (MICCAI) to attract significant contributions attacking the challenges in Medical Imaging and Healthcare. In this seminar, we will be discussing the relevant papers on Federated Learning with an emphasis on the papers tackling the common challenges in Medical Imaging, e.g. data heterogeneity, domain shift, and non-iid distributed data.


  • Interested students should attend the preliminary meeting to enlist in the course.
  • Students can only register through TUM Matching Platform themselves if the maximum number of participants hasn’t been reached (please pay attention to the Deadlines).
  • A maximum number of participants: 12.


In this Master Seminar, each student is asked to send three preferences from the list, then he will be assigned one paper. In order to successfully complete the seminar, participants have to fulfill these requirements:

  • Presentation: The selected paper is presented to the other participants.
  • Blog Post: A blog post of 1000-1500 words excluding references should be submitted before the deadline.
  • Attendance: Participants have to participate actively in all seminar sessions.

The students are required to attend each seminar presentation which will be held during this course. Each presentation is followed by a discussion and everyone is encouraged to actively participate. The blog post must include all references used and must be written completely in your own words. Copy and paste will not be tolerated. Both the blog post and presentation have to be written in English.

Submission Deadline: You have to submit both the presentation two weeks right after your presentation session. The deadline of the blog post is moved to 1st Feb. 2021.

Guidelines: I could not find better than this guidelines to prepare for your presentation. The only difference is that you need to plan for 30 minutes for 1-4, and 10 minutes for 5). Nevertheless, I have prepared a few slides acting as a guidelines for your presentation and blog posts.


Date Session: Topic Speakers / Presenters Slides
17.07.2020 (11:00 AM) Preliminary Meeting Slides
Online Paper Assignment Guidelines
09.11.2020 @ 11:00 AM Federated Learning; Challenges, Methods, and Future I Invited Talk: Federated Learning: Collaborative AI without Exposing Patient Data Nicola Rieke from NVIDIA
16.11.2020 Federated Learning; Challenges, Methods, and Future II Ünay, Sánchez Clemente
23.11.2020 Data Heterogeneity I Lin, Raether
30.11.2020 Data Heterogeneity II Stoican, Schwarz
07.12.2020 System Heterogeneity and Privacy Issues I Invited Talk: Secure, privacy-preserving and federated machine learning in medical imaging George Kaissis from Klinikum rechts der Isar
14.12.2020 System Heterogeneity and Privacy Issues II Qian, Heidmann
21.12.2020 Data Heterogeneity III Spannagl, Arfaoui
11.01.2021 Federated Learning with Medical Imaging I Zehra ( NeurIPS'20 Paper), Cosmin ( MICCAIW'20 Paper)
18.01.2021 Federated Learning with Medical Imaging II Boysen
25.01.2021 Federated Learning with Medical Imaging III Invited Talk: Federated Learning with Heterogeneous data in Healthcare –Jean Ogier du Terrail from Owkin

List of Topics and Papers

Topic No Title Conference/Journal Tutor Student (Last name) Link
Intro. to FL 1 FedAvg: Communication-Efficient Learning of Deep Networks from Decentralized Data AISTATS, 2016 Ünay arXiv
2 The Future of Digital Health with Federated Learning arXiv, 2020 Invited Speaker arXiv
Challenges 3 Federated Learning: Challenges, Methods, and Future Directions IEEE Signal Processing Magazine, 2020 Sánchez Clemente arXiv
4 On the Convergence of FedAvg on Non-IID Data ICLR 2020 PDF
Data Heterogeneity 5 FedMA: Federated Learning with Matched Averaging ICLR 2020 Lin PDF
6 Federated Adversarial Domain Adaptation ICLR 2020 Spannagl PDF
7 Federated optimization in heterogeneous networks MLSys 2020 Raether PDF
8 FedAwS: Federated Learning with Only Positive Labels ICML 2020 PDF
9 SCAFFOLD: Stochastic Controlled Averaging for Federated Learning ICML 2020 Stoican PDF
10 Federated Visual Classification with Real-World Data Distribution CVPR 2020 Schwarz PDF
System Heterogeneity 11 Federated Multi-Task Learning NeurIPS 2017 Qian PDF
12 Variational Federated Multi-Task Learning arXiv 2019 Heidmann arXiv
Privacy-Issues 13 Secure, privacy-preserving and federated machine learning in medical imaging Nature MI Invited Speaker HTML
14 Differentially Private Meta-Learning ICLR 2020 PDF
Explainability and Robustness 15 The Non-IID Data Quagmire of Decentralized Machine Learning ICML 2020 Arfaoui PDF
16 DBA: Distributed Backdoor Attacks against Federated Learning ICLR 2020 PDF
Open Problems in FL Advances and Open Problems in Federated Learning arXiv PDF
Federated Learning with Medical Imaging 17 Privacy-preserving Federated Brain Tumour Segmentation MICCAIW 2019 Boysen HTML
Multi-institutional Deep Learning Modeling Without Sharing Patient Data: A Feasibility Study on Brain Tumor Segmentation MICCAIW 2019 Boysen HTML
18 Federated Learning in Distributed Medical Databases: Meta-Analysis of Large-Scale Subcortical Brain Data ISBI 2019 Hofmann HTML
Inverse Distance Aggregation for Federated Learning with Non-IID Data MICCAIW 2020 Hofmann PDF


Contact us

If you have any questions regarding the course, please do not hesitate to contact us at

Shadi Albarqouni
Shadi Albarqouni
Professor of Computational Medical Imaging Research at University of Bonn | AI Young Investigator Group Leader at Helmholtz AI